Perception of difficulty with Random Curve

## Ignorer les outliers relatifs à l'utilisation de l'échelle de confiance :  TRUE

## Résultats basés sur la l'échelle de confiance :  TRUE

## Nombre de participants à l'expérimentation :  58

## Nombre de participants se déclarant comme joueurs :  29

## Nombre de femmes se déclarant comme joueuses :  3

## Age médian des joueurs :  15

Removing Outliers

## [1] "Outliers BET STANDARD DEVIATION: 3qq8dp8jk, 79pn8m6v8, e58u3sinl, urgv6o806"

## Empty data.table (0 rows) of 1 col: IDjoueur

## Empty data.table (0 rows) of 1 col: IDjoueur

## Empty data.table (0 rows) of 1 col: IDjoueur

## [1] "Outliers BET SAVED SHEEPS: "

## Empty data.table (0 rows) of 1 col: IDjoueur

## Empty data.table (0 rows) of 1 col: IDjoueur

## Empty data.table (0 rows) of 1 col: IDjoueur

## [1] "Outliers CS STANDARD DEVIATION: 3qq8dp8jk, 79pn8m6v8, e58u3sinl, urgv6o806, 9b3ph38yc, 9b3ph38yc, a6dfu5ljd, a6dfu5ljd, bzrji9dqz, dyg7cga2o, dyg7cga2o, ejodnl05c, kctu3te1y, tmxmxmwhi, zp9bc59o5, zv35u39vc"

## Empty data.table (0 rows) of 1 col: IDjoueur

## [1] "Outliers CS NULL: 3qq8dp8jk, 79pn8m6v8, e58u3sinl, urgv6o806, 9b3ph38yc, 9b3ph38yc, 9b3ph38yc, a6dfu5ljd, a6dfu5ljd, a6dfu5ljd, bzrji9dqz, bzrji9dqz, dyg7cga2o, dyg7cga2o, dyg7cga2o, ejodnl05c, kctu3te1y, kctu3te1y, m4ye7uz5h, qzh5zi9e8, tmxmxmwhi, tmxmxmwhi, zp9bc59o5, zp9bc59o5, zv35u39vc"

## Empty data.table (0 rows) of 1 col: IDjoueur

## [1] "Total number of participants :  58"

## [1] "Total number of outliers:  15"

## [1] "- total number of outliers motor task:  11"

## [1] "- total number of outliers perceptive task:  6"

## [1] "- total number of outliers logical task:  8"

## [1] "Total number of participants after removing outliers:  55"

## [1] "- motor:  47"

## [1] "- perceptive:  50"

## [1] "- logical:  52"

Modeling difficulty

Modeling objective difficulty for motor task

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
##    Data: DT
## 
##      AIC      BIC   logLik deviance df.resid 
##   1669.2   1690.0   -830.6   1661.2     1359 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.8343 -0.7720  0.3062  0.7571  2.7501 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  IDjoueur (Intercept) 0.4686   0.6846  
## Number of obs: 1363, groups:  IDjoueur, 47
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -0.9982     0.1974  -5.057 4.27e-07 ***
## difficulty    2.8413     0.2301  12.346  < 2e-16 ***
## timeNorm     -0.5530     0.2179  -2.538   0.0112 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr) dffclt
## difficulty -0.549       
## timeNorm   -0.577 -0.022

## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.

## 
##  Logique2   Motrice Sensoriel 
##         0      1363         0 
## [1] "Player levels from ranef:"
##   (Intercept)      
##  Min.   :-0.96344  
##  1st Qu.:-0.37670  
##  Median :-0.08364  
##  Mean   :-0.00173  
##  3rd Qu.: 0.21652  
##  Max.   : 1.57591  
## [1] "Intercept: -0.998 4.3e-07 ***"
## [1] "Difficulty: 2.84 5.1e-35 ***"
## [1] "Time: -0.553 0.011 *"
## [1] "R2 fixed: 0.16"
## [1] "R2 mixed: 0.26"
## [1] "Cross Val: 0.68"
## [1] "AIC: 1700"

##          0%         25%         50%         75%        100% 
## -1.57590870 -0.21652213  0.08364306  0.37669604  0.96343672

##          0%         25%         50%         75%        100% 
## -1.57590870 -0.21652213  0.08364306  0.37669604  0.96343672

## `geom_smooth()` using method = 'gam'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

Modeling objective difficulty for sensory task

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
##    Data: DT
## 
##      AIC      BIC   logLik deviance df.resid 
##   1123.8   1144.9   -557.9   1115.8     1446 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -6.3468 -0.3664  0.1130  0.3424  6.3198 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  IDjoueur (Intercept) 0.788    0.8877  
## Number of obs: 1450, groups:  IDjoueur, 50
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -3.1933     0.2727 -11.710   <2e-16 ***
## difficulty    8.1870     0.4266  19.193   <2e-16 ***
## timeNorm     -0.4773     0.2844  -1.679   0.0932 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr) dffclt
## difficulty -0.632       
## timeNorm   -0.506 -0.072

## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control
## $checkConv, : Model failed to converge with max|grad| = 0.0677766 (tol =
## 0.001, component 1)

## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
##  - Rescale variables?

## The result is correct only if all data used by the model has not changed since model was fitted.

## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model failed to converge with max|grad| = 0.0677766 (tol = 0.001, component 1)

## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
##  - Rescale variables?

## The result is correct only if all data used by the model has not changed since model was fitted.

## 
##  Logique2   Motrice Sensoriel 
##         0         0      1450 
## [1] "Player levels from ranef:"
##   (Intercept)        
##  Min.   :-1.7107216  
##  1st Qu.:-0.4707794  
##  Median : 0.0814227  
##  Mean   :-0.0009546  
##  3rd Qu.: 0.4563319  
##  Max.   : 1.5481412  
## [1] "Intercept: -3.19 1.1e-31 ***"
## [1] "Difficulty: 8.19 4.2e-82 ***"
## [1] "Time: -0.477 0.093 ."
## [1] "R2 fixed: 0.33"
## [1] "R2 mixed: 0.47"
## [1] "Cross Val: 0.82"
## [1] "AIC: 1100"

##          0%         25%         50%         75%        100% 
## -1.54814118 -0.45633190 -0.08142269  0.47077941  1.71072157

##          0%         25%         50%         75%        100% 
## -1.54814118 -0.45633190 -0.08142269  0.47077941  1.71072157

## `geom_smooth()` using method = 'gam'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

Modeling objective difficulty for logical task

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
##    Data: DT
## 
##      AIC      BIC   logLik deviance df.resid 
##   1426.5   1447.8   -709.2   1418.5     1504 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -5.9435 -0.5021 -0.1156  0.5089  4.9862 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  IDjoueur (Intercept) 1.577    1.256   
## Number of obs: 1508, groups:  IDjoueur, 52
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -1.8650     0.2652  -7.033 2.01e-12 ***
## difficulty    5.6686     0.3206  17.680  < 2e-16 ***
## timeNorm     -1.9313     0.2573  -7.507 6.04e-14 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr) dffclt
## difficulty -0.496       
## timeNorm   -0.378 -0.227

## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.

## 
##  Logique2   Motrice Sensoriel 
##      1508         0         0 
## [1] "Player levels from ranef:"
##   (Intercept)        
##  Min.   :-1.7902825  
##  1st Qu.:-0.7784485  
##  Median :-0.3355504  
##  Mean   :-0.0003123  
##  3rd Qu.: 0.7369882  
##  Max.   : 3.1275697  
## [1] "Intercept: -1.86 2e-12 ***"
## [1] "Difficulty: 5.67 6e-70 ***"
## [1] "Time: -1.93 6e-14 ***"
## [1] "R2 fixed: 0.38"
## [1] "R2 mixed: 0.58"
## [1] "Cross Val: 0.79"
## [1] "AIC: 1400"

##         0%        25%        50%        75%       100% 
## -3.1275697 -0.7369882  0.3355504  0.7784485  1.7902825

##         0%        25%        50%        75%       100% 
## -3.1275697 -0.7369882  0.3355504  0.7784485  1.7902825

## `geom_smooth()` using method = 'gam'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

Influence of Player Profiles

Player profiles

Influence of Player Profiles

Objective level and player profile

Playing video games in general and level for each task

## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.37495, p-value = 0.7077
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.04294701

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.91836, p-value = 0.3584
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.1023712

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.30458, p-value = 0.7607
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.03301126

Playing board games in general and level for each task

## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.99227, p-value = 0.3211
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##    tau 
## 0.1118

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.31221, p-value = 0.7549
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.03415935

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.79975, p-value = 0.4239
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.08596507

Self efficacy and level for each task

## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties

## Warning: Removed 23 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.24953, p-value = 0.8029
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.03718731

## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties

## Warning in cor.test.default(Y, X, method = "kendall"): Removed 23 rows
## containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 2.4333, p-value = 0.01496
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.3398094 
## 
## [1] "self.eff.on.level.s 0.34 0.015 *"

## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties

## Warning: Removed 26 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.51036, p-value = 0.6098
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.07281435

Risk aversion and level for each task

## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.3418, p-value = 0.1797
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1465938

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 2.0586, p-value = 0.03953
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.2157658 
## 
## [1] "risk.av.on.level.s 0.22 0.04 *"

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.3062, p-value = 0.1915
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1347244

Age and level for each task

## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties

## Warning: Removed 1 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -1.3062, p-value = 0.1915
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.1372263

## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties

## Warning in cor.test.default(Y, X, method = "kendall"): Removed 1 rows
## containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.8963, p-value = 0.05791
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1937968 
## 
## [1] "age.on.level.s 0.19 0.058 ."

## Warning: Removed 1 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.2774, p-value = 0.2015
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1275074

Sex and level for each task

## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -2.0369, p-value = 0.04166
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.2478106 
## 
## [1] "sexe.on.level.m -0.25 0.042 *"

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.083189, p-value = 0.9337
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## 0.009799919

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.26928, p-value = 0.7877
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.03108211

## 
##  Wilcoxon rank sum test
## 
## data:  B and A
## W = 163, p-value = 0.04192
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.73654416 -0.04033621
## sample estimates:
## difference in location 
##             -0.3800085 
## 
## [1] "sexe.on.level.m.2 -0.38 0.042 * mean(A): 0.15 mean(B): -0.27"

## 
##  Wilcoxon rank sum test
## 
## data:  B and A
## W = 276, p-value = 0.9426
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.4761356  0.5715623
## sample estimates:
## difference in location 
##             0.01423148

## 
##  Wilcoxon rank sum test
## 
## data:  B and A
## W = 292, p-value = 0.7971
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.8271570  0.5994594
## sample estimates:
## difference in location 
##            -0.04046848

Influence of Objective difficulty on Subjective Difficulty

All tasks

## [1] "all"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125          0.069 44   0.0032 **
##  2:      0.09375          0.110 53 0.00022 ***
##  3:      0.15625          0.094 54   0.0016 **
##  4:      0.21875          0.110 52 0.00013 ***
##  5:      0.28125          0.097 54   0.0015 **
##  6:      0.34375          0.110 52 3.2e-05 ***
##  7:      0.40625          0.074 53     0.044 *
##  8:      0.46875          0.019 52     0.46 :(
##  9:      0.53125         -0.024 51     0.41 :(
## 10:      0.59375         -0.047 55     0.024 *
## 11:      0.65625         -0.073 52   0.0013 **
## 12:      0.71875         -0.130 54 8.7e-06 ***
## 13:      0.78125         -0.160 53 2.1e-07 ***
## 14:      0.84375         -0.210 52   3e-08 ***
## 15:      0.90625         -0.230 54 7.8e-10 ***
## 16:      0.96875         -0.170 54 3.7e-09 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 44   0.0032 **
##  2: 53 0.00022 ***
##  3: 54   0.0016 **
##  4: 52 0.00013 ***
##  5: 54   0.0015 **
##  6: 52 3.2e-05 ***
##  7: 53     0.044 *
##  8: 52     0.46 :(
##  9: 51     0.41 :(
## 10: 55     0.024 *
## 11: 52   0.0013 **
## 12: 54 8.7e-06 ***
## 13: 53 2.1e-07 ***
## 14: 52   3e-08 ***
## 15: 54 7.8e-10 ***
## 16: 54 3.7e-09 ***
## [1] 52.4
## [1] 2.5

## [1] "good"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125          0.078 35     0.015 *
##  2:      0.09375          0.110 39 0.00096 ***
##  3:      0.15625          0.098 41   0.0074 **
##  4:      0.21875          0.100 38   0.0064 **
##  5:      0.28125          0.100 35   0.0077 **
##  6:      0.34375          0.110 34 0.00089 ***
##  7:      0.40625          0.069 38     0.037 *
##  8:      0.46875          0.031 37     0.051 .
##  9:      0.53125          0.019 35     0.66 :(
## 10:      0.59375         -0.044 40     0.25 :(
## 11:      0.65625         -0.073 36     0.016 *
## 12:      0.71875         -0.180 37 4.3e-05 ***
## 13:      0.78125         -0.180 35 0.00032 ***
## 14:      0.84375         -0.220 30 6.4e-05 ***
## 15:      0.90625         -0.250 29 1.5e-05 ***
## 16:      0.96875         -0.160 19   0.0057 **
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 35     0.015 *
##  2: 39 0.00096 ***
##  3: 41   0.0074 **
##  4: 38   0.0064 **
##  5: 35   0.0077 **
##  6: 34 0.00089 ***
##  7: 38     0.037 *
##  8: 37     0.051 .
##  9: 35     0.66 :(
## 10: 40     0.25 :(
## 11: 36     0.016 *
## 12: 37 4.3e-05 ***
## 13: 35 0.00032 ***
## 14: 30 6.4e-05 ***
## 15: 29 1.5e-05 ***
## 16: 19   0.0057 **
## [1] 34.9
## [1] 5.3

## [1] "medium"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125          0.052 24     0.19 :(
##  2:      0.09375          0.081 27     0.057 .
##  3:      0.15625          0.010 26     0.94 :(
##  4:      0.21875          0.081 32     0.032 *
##  5:      0.28125          0.094 33     0.12 :(
##  6:      0.34375          0.069 30     0.099 .
##  7:      0.40625          0.069 36     0.35 :(
##  8:      0.46875         -0.052 34     0.57 :(
##  9:      0.53125         -0.010 33     0.82 :(
## 10:      0.59375         -0.077 35     0.073 .
## 11:      0.65625         -0.130 36   0.0045 **
## 12:      0.71875         -0.085 33   0.0059 **
## 13:      0.78125         -0.130 37 0.00051 ***
## 14:      0.84375         -0.240 35 9.9e-06 ***
## 15:      0.90625         -0.220 34 1.8e-06 ***
## 16:      0.96875         -0.160 34 3.6e-06 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 24     0.19 :(
##  2: 27     0.057 .
##  3: 26     0.94 :(
##  4: 32     0.032 *
##  5: 33     0.12 :(
##  6: 30     0.099 .
##  7: 36     0.35 :(
##  8: 34     0.57 :(
##  9: 33     0.82 :(
## 10: 35     0.073 .
## 11: 36   0.0045 **
## 12: 33   0.0059 **
## 13: 37 0.00051 ***
## 14: 35 9.9e-06 ***
## 15: 34 1.8e-06 ***
## 16: 34 3.6e-06 ***
## [1] 32.4
## [1] 3.79

## [1] "bad"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125          0.019  2        1 :(
##  2:      0.09375          0.081 11     0.12 :(
##  3:      0.15625          0.120 13      0.03 *
##  4:      0.21875          0.089  8     0.29 :(
##  5:      0.28125         -0.031  9        1 :(
##  6:      0.34375          0.130  7     0.048 *
##  7:      0.40625          0.094  9     0.33 :(
##  8:      0.46875          0.031  7     0.55 :(
##  9:      0.53125         -0.210 11     0.011 *
## 10:      0.59375         -0.094 13     0.12 :(
## 11:      0.65625         -0.031 11     0.39 :(
## 12:      0.71875         -0.160 14     0.044 *
## 13:      0.78125         -0.130 12     0.054 .
## 14:      0.84375         -0.160 13     0.014 *
## 15:      0.90625         -0.200 16   0.0065 **
## 16:      0.96875         -0.240 15 0.00087 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1:  2        1 :(
##  2: 11     0.12 :(
##  3: 13      0.03 *
##  4:  8     0.29 :(
##  5:  9        1 :(
##  6:  7     0.048 *
##  7:  9     0.33 :(
##  8:  7     0.55 :(
##  9: 11     0.011 *
## 10: 13     0.12 :(
## 11: 11     0.39 :(
## 12: 14     0.044 *
## 13: 12     0.054 .
## 14: 13     0.014 *
## 15: 16   0.0065 **
## 16: 15 0.00087 ***
## [1] 10.7
## [1] 3.57

Motor task

## [1] "all"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n    pval
##  1:      0.03125             NA  0      NA
##  2:      0.09375        -0.0440  5 0.78 :(
##  3:      0.15625        -0.0730 19 0.13 :(
##  4:      0.21875         0.0190 35 0.65 :(
##  5:      0.28125         0.0350 40 0.36 :(
##  6:      0.34375         0.0900 40 0.018 *
##  7:      0.40625         0.0540 42 0.19 :(
##  8:      0.46875         0.0560 42 0.098 .
##  9:      0.53125         0.0440 43 0.15 :(
## 10:      0.59375        -0.0100 45 0.91 :(
## 11:      0.65625        -0.0560 44 0.041 *
## 12:      0.71875        -0.0440 43 0.076 .
## 13:      0.78125        -0.0810 38 0.032 *
## 14:      0.84375        -0.1400 23 0.023 *
## 15:      0.90625        -0.0063  7 0.44 :(
## 16:      0.96875        -0.2400  4  0.2 :(
## [1] "mean and sd of nb players per bin"
##     nb    pval
##  1:  5 0.78 :(
##  2: 19 0.13 :(
##  3: 35 0.65 :(
##  4: 40 0.36 :(
##  5: 40 0.018 *
##  6: 42 0.19 :(
##  7: 42 0.098 .
##  8: 43 0.15 :(
##  9: 45 0.91 :(
## 10: 44 0.041 *
## 11: 43 0.076 .
## 12: 38 0.032 *
## 13: 23 0.023 *
## 14:  7 0.44 :(
## 15:  4  0.2 :(
## [1] 31.3
## [1] 15.4

## Warning: Removed 1 rows containing missing values (geom_point).

## Warning: Removed 1 rows containing missing values (geom_errorbar).

## [1] "good"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n    pval
##  1:      0.03125             NA  0      NA
##  2:      0.09375        -0.0440  5 0.78 :(
##  3:      0.15625        -0.0730 17 0.057 .
##  4:      0.21875        -0.0190 21 0.61 :(
##  5:      0.28125         0.0190 21 0.42 :(
##  6:      0.34375         0.1100 21 0.023 *
##  7:      0.40625         0.0600 20 0.16 :(
##  8:      0.46875         0.1100 20 0.024 *
##  9:      0.53125         0.1000 19 0.067 .
## 10:      0.59375         0.0880 20 0.18 :(
## 11:      0.65625         0.0051 20    1 :(
## 12:      0.71875        -0.0190 17  0.6 :(
## 13:      0.78125        -0.0560 12 0.25 :(
## 14:      0.84375             NA  0      NA
## 15:      0.90625             NA  0      NA
## 16:      0.96875             NA  0      NA
## [1] "mean and sd of nb players per bin"
##     nb    pval
##  1:  5 0.78 :(
##  2: 17 0.057 .
##  3: 21 0.61 :(
##  4: 21 0.42 :(
##  5: 21 0.023 *
##  6: 20 0.16 :(
##  7: 20 0.024 *
##  8: 19 0.067 .
##  9: 20 0.18 :(
## 10: 20    1 :(
## 11: 17  0.6 :(
## 12: 12 0.25 :(
## [1] 17.8
## [1] 4.77

## Warning: Removed 4 rows containing missing values (geom_point).

## Warning: Removed 4 rows containing missing values (geom_errorbar).

## [1] "medium"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

##     obj.diff.bin delta.obj.subj  n      pval
##  1:      0.03125             NA  0        NA
##  2:      0.09375             NA  0        NA
##  3:      0.15625          0.290  2      1 :(
##  4:      0.21875          0.069 14   0.29 :(
##  5:      0.28125          0.069 19   0.46 :(
##  6:      0.34375          0.076 19   0.32 :(
##  7:      0.40625          0.020 21   0.83 :(
##  8:      0.46875         -0.019 20   0.93 :(
##  9:      0.53125          0.019 20   0.69 :(
## 10:      0.59375         -0.077 20   0.076 .
## 11:      0.65625         -0.160 20 0.0074 **
## 12:      0.71875         -0.056 21   0.088 .
## 13:      0.78125         -0.081 21   0.21 :(
## 14:      0.84375         -0.160 18   0.029 *
## 15:      0.90625         -0.210  2    0.5 :(
## 16:      0.96875             NA  0        NA
## [1] "mean and sd of nb players per bin"
##     nb      pval
##  1:  2      1 :(
##  2: 14   0.29 :(
##  3: 19   0.46 :(
##  4: 19   0.32 :(
##  5: 21   0.83 :(
##  6: 20   0.93 :(
##  7: 20   0.69 :(
##  8: 20   0.076 .
##  9: 20 0.0074 **
## 10: 21   0.088 .
## 11: 21   0.21 :(
## 12: 18   0.029 *
## 13:  2    0.5 :(
## [1] 16.7
## [1] 6.77

## Warning: Removed 3 rows containing missing values (geom_point).

## Warning: Removed 3 rows containing missing values (geom_errorbar).

## [1] "bad"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj n    pval
##  1:      0.03125             NA 0      NA
##  2:      0.09375             NA 0      NA
##  3:      0.15625             NA 0      NA
##  4:      0.21875             NA 0      NA
##  5:      0.28125             NA 0      NA
##  6:      0.34375             NA 0      NA
##  7:      0.40625             NA 1      NA
##  8:      0.46875         0.1800 2  0.5 :(
##  9:      0.53125        -0.0310 4 0.58 :(
## 10:      0.59375        -0.0270 5 0.78 :(
## 11:      0.65625        -0.0059 4    1 :(
## 12:      0.71875        -0.0520 5 0.62 :(
## 13:      0.78125        -0.0940 5 0.31 :(
## 14:      0.84375        -0.0440 5 0.59 :(
## 15:      0.90625        -0.0062 5    1 :(
## 16:      0.96875        -0.2400 4  0.2 :(
## [1] "mean and sd of nb players per bin"
##    nb    pval
## 1:  2  0.5 :(
## 2:  4 0.58 :(
## 3:  5 0.78 :(
## 4:  4    1 :(
## 5:  5 0.62 :(
## 6:  5 0.31 :(
## 7:  5 0.59 :(
## 8:  5    1 :(
## 9:  4  0.2 :(
## [1] 4.33
## [1] 1

## Warning: Removed 7 rows containing missing values (geom_point).

## Warning: Removed 7 rows containing missing values (geom_errorbar).

Sensory task

## [1] "all"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125          0.024 38     0.29 :(
##  2:      0.09375          0.031 47      0.2 :(
##  3:      0.15625          0.044 45     0.47 :(
##  4:      0.21875          0.031 34     0.63 :(
##  5:      0.28125          0.019 32     0.99 :(
##  6:      0.34375         -0.019 28     0.77 :(
##  7:      0.40625         -0.031 32     0.61 :(
##  8:      0.46875         -0.120 31     0.044 *
##  9:      0.53125         -0.180 27   0.0049 **
## 10:      0.59375         -0.190 34 0.00092 ***
## 11:      0.65625         -0.180 33 0.00027 ***
## 12:      0.71875         -0.220 34 5.2e-05 ***
## 13:      0.78125         -0.260 32   9e-06 ***
## 14:      0.84375         -0.270 39 1.6e-05 ***
## 15:      0.90625         -0.210 47 4.8e-08 ***
## 16:      0.96875         -0.094 50 1.2e-06 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 38     0.29 :(
##  2: 47      0.2 :(
##  3: 45     0.47 :(
##  4: 34     0.63 :(
##  5: 32     0.99 :(
##  6: 28     0.77 :(
##  7: 32     0.61 :(
##  8: 31     0.044 *
##  9: 27   0.0049 **
## 10: 34 0.00092 ***
## 11: 33 0.00027 ***
## 12: 34 5.2e-05 ***
## 13: 32   9e-06 ***
## 14: 39 1.6e-05 ***
## 15: 47 4.8e-08 ***
## 16: 50 1.2e-06 ***
## [1] 36.4
## [1] 7.16

## [1] "good"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n      pval
##  1:      0.03125          0.060 13   0.48 :(
##  2:      0.09375         -0.044 12   0.72 :(
##  3:      0.15625          0.094 12    0.4 :(
##  4:      0.21875         -0.085  6    0.4 :(
##  5:      0.28125          0.140  9   0.28 :(
##  6:      0.34375         -0.094  7   0.27 :(
##  7:      0.40625         -0.110  9   0.12 :(
##  8:      0.46875         -0.220  9   0.096 .
##  9:      0.53125         -0.200  6   0.31 :(
## 10:      0.59375         -0.240  9   0.043 *
## 11:      0.65625         -0.360  8   0.014 *
## 12:      0.71875         -0.470 10 0.0055 **
## 13:      0.78125         -0.310  7   0.051 .
## 14:      0.84375         -0.220 10   0.052 .
## 15:      0.90625         -0.160 11   0.014 *
## 16:      0.96875         -0.120 13   0.12 :(
## [1] "mean and sd of nb players per bin"
##     nb      pval
##  1: 13   0.48 :(
##  2: 12   0.72 :(
##  3: 12    0.4 :(
##  4:  6    0.4 :(
##  5:  9   0.28 :(
##  6:  7   0.27 :(
##  7:  9   0.12 :(
##  8:  9   0.096 .
##  9:  6   0.31 :(
## 10:  9   0.043 *
## 11:  8   0.014 *
## 12: 10 0.0055 **
## 13:  7   0.051 .
## 14: 10   0.052 .
## 15: 11   0.014 *
## 16: 13   0.12 :(
## [1] 9.44
## [1] 2.31

## [1] "medium"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125          0.019 23     0.58 :(
##  2:      0.09375          0.031 24     0.46 :(
##  3:      0.15625         -0.056 20     0.048 *
##  4:      0.21875          0.031 20     0.84 :(
##  5:      0.28125         -0.031 14     0.66 :(
##  6:      0.34375         -0.023 15     0.71 :(
##  7:      0.40625          0.019 16     0.74 :(
##  8:      0.46875         -0.094 17      0.2 :(
##  9:      0.53125         -0.130 14      0.1 :(
## 10:      0.59375         -0.240 16     0.032 *
## 11:      0.65625         -0.180 18     0.012 *
## 12:      0.71875         -0.190 13   0.0077 **
## 13:      0.78125         -0.260 18 0.00043 ***
## 14:      0.84375         -0.340 19   0.0027 **
## 15:      0.90625         -0.210 24 9.5e-05 ***
## 16:      0.96875         -0.063 24 0.00095 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 23     0.58 :(
##  2: 24     0.46 :(
##  3: 20     0.048 *
##  4: 20     0.84 :(
##  5: 14     0.66 :(
##  6: 15     0.71 :(
##  7: 16     0.74 :(
##  8: 17      0.2 :(
##  9: 14      0.1 :(
## 10: 16     0.032 *
## 11: 18     0.012 *
## 12: 13   0.0077 **
## 13: 18 0.00043 ***
## 14: 19   0.0027 **
## 15: 24 9.5e-05 ***
## 16: 24 0.00095 ***
## [1] 18.4
## [1] 3.78

## [1] "bad"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n      pval
##  1:      0.03125          0.019  2      1 :(
##  2:      0.09375          0.081 11   0.12 :(
##  3:      0.15625          0.120 13    0.03 *
##  4:      0.21875          0.089  8   0.29 :(
##  5:      0.28125         -0.031  9      1 :(
##  6:      0.34375          0.090  6    0.09 .
##  7:      0.40625          0.094  7   0.67 :(
##  8:      0.46875         -0.044  5      1 :(
##  9:      0.53125         -0.280  7   0.021 *
## 10:      0.59375         -0.094  9   0.087 .
## 11:      0.65625         -0.160  7   0.34 :(
## 12:      0.71875         -0.130 11   0.26 :(
## 13:      0.78125         -0.180  7   0.11 :(
## 14:      0.84375         -0.190 10   0.031 *
## 15:      0.90625         -0.290 12 0.0041 **
## 16:      0.96875         -0.200 13 0.0021 **
## [1] "mean and sd of nb players per bin"
##     nb      pval
##  1:  2      1 :(
##  2: 11   0.12 :(
##  3: 13    0.03 *
##  4:  8   0.29 :(
##  5:  9      1 :(
##  6:  6    0.09 .
##  7:  7   0.67 :(
##  8:  5      1 :(
##  9:  7   0.021 *
## 10:  9   0.087 .
## 11:  7   0.34 :(
## 12: 11   0.26 :(
## 13:  7   0.11 :(
## 14: 10   0.031 *
## 15: 12 0.0041 **
## 16: 13 0.0021 **
## [1] 8.56
## [1] 3.03

Logical task

## [1] "all"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125          0.089 35     0.017 *
##  2:      0.09375          0.160 40 5.2e-05 ***
##  3:      0.15625          0.150 40 0.00025 ***
##  4:      0.21875          0.230 42 9.5e-06 ***
##  5:      0.28125          0.220 34 0.00028 ***
##  6:      0.34375          0.160 39 5.5e-05 ***
##  7:      0.40625          0.094 44     0.011 *
##  8:      0.46875          0.031 39     0.024 *
##  9:      0.53125         -0.031 37     0.21 :(
## 10:      0.59375         -0.019 41     0.77 :(
## 11:      0.65625         -0.018 39     0.68 :(
## 12:      0.71875         -0.100 38    0.002 **
## 13:      0.78125         -0.160 43 9.5e-05 ***
## 14:      0.84375         -0.220 41 6.5e-07 ***
## 15:      0.90625         -0.260 40 3.4e-07 ***
## 16:      0.96875         -0.340 25 1.4e-05 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 35     0.017 *
##  2: 40 5.2e-05 ***
##  3: 40 0.00025 ***
##  4: 42 9.5e-06 ***
##  5: 34 0.00028 ***
##  6: 39 5.5e-05 ***
##  7: 44     0.011 *
##  8: 39     0.024 *
##  9: 37     0.21 :(
## 10: 41     0.77 :(
## 11: 39     0.68 :(
## 12: 38    0.002 **
## 13: 43 9.5e-05 ***
## 14: 41 6.5e-07 ***
## 15: 40 3.4e-07 ***
## 16: 25 1.4e-05 ***
## [1] 38.6
## [1] 4.47

## [1] "good"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125          0.069 31     0.027 *
##  2:      0.09375          0.160 34   2e-04 ***
##  3:      0.15625          0.140 31   0.0016 **
##  4:      0.21875          0.230 31   1e-04 ***
##  5:      0.28125          0.190 24   0.0081 **
##  6:      0.34375          0.160 26   0.0059 **
##  7:      0.40625          0.094 29     0.079 .
##  8:      0.46875          0.031 27     0.026 *
##  9:      0.53125         -0.031 25     0.53 :(
## 10:      0.59375         -0.056 26     0.21 :(
## 11:      0.65625         -0.081 25     0.26 :(
## 12:      0.71875         -0.150 24   0.0033 **
## 13:      0.78125         -0.180 28 0.00097 ***
## 14:      0.84375         -0.220 25 0.00014 ***
## 15:      0.90625         -0.310 22 0.00016 ***
## 16:      0.96875         -0.320  7     0.034 *
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 31     0.027 *
##  2: 34   2e-04 ***
##  3: 31   0.0016 **
##  4: 31   1e-04 ***
##  5: 24   0.0081 **
##  6: 26   0.0059 **
##  7: 29     0.079 .
##  8: 27     0.026 *
##  9: 25     0.53 :(
## 10: 26     0.21 :(
## 11: 25     0.26 :(
## 12: 24   0.0033 **
## 13: 28 0.00097 ***
## 14: 25 0.00014 ***
## 15: 22 0.00016 ***
## 16:  7     0.034 *
## [1] 25.9
## [1] 6.01

## [1] "medium"

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125          0.190  4     0.58 :(
##  2:      0.09375          0.360  6     0.14 :(
##  3:      0.15625          0.170  9     0.053 .
##  4:      0.21875          0.250 11     0.036 *
##  5:      0.28125          0.340 10     0.011 *
##  6:      0.34375          0.180 12   0.0026 **
##  7:      0.40625          0.160 14     0.077 .
##  8:      0.46875          0.055 12      0.5 :(
##  9:      0.53125         -0.031 11     0.29 :(
## 10:      0.59375          0.031 14     0.26 :(
## 11:      0.65625          0.069 13     0.36 :(
## 12:      0.71875         -0.019 13     0.43 :(
## 13:      0.78125         -0.150 15     0.043 *
## 14:      0.84375         -0.220 16   0.0014 **
## 15:      0.90625         -0.240 17   0.0011 **
## 16:      0.96875         -0.340 17 0.00031 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1:  4     0.58 :(
##  2:  6     0.14 :(
##  3:  9     0.053 .
##  4: 11     0.036 *
##  5: 10     0.011 *
##  6: 12   0.0026 **
##  7: 14     0.077 .
##  8: 12      0.5 :(
##  9: 11     0.29 :(
## 10: 14     0.26 :(
## 11: 13     0.36 :(
## 12: 13     0.43 :(
## 13: 15     0.043 *
## 14: 16   0.0014 **
## 15: 17   0.0011 **
## 16: 17 0.00031 ***
## [1] 12.1
## [1] 3.65

## [1] "bad"

##     obj.diff.bin delta.obj.subj n pval
##  1:      0.03125             NA 0   NA
##  2:      0.09375             NA 0   NA
##  3:      0.15625             NA 0   NA
##  4:      0.21875             NA 0   NA
##  5:      0.28125             NA 0   NA
##  6:      0.34375             NA 1   NA
##  7:      0.40625             NA 1   NA
##  8:      0.46875             NA 0   NA
##  9:      0.53125             NA 1   NA
## 10:      0.59375             NA 1   NA
## 11:      0.65625             NA 1   NA
## 12:      0.71875             NA 1   NA
## 13:      0.78125             NA 0   NA
## 14:      0.84375             NA 0   NA
## 15:      0.90625             NA 1   NA
## 16:      0.96875             NA 1   NA
## [1] "mean and sd of nb players per bin"
## Empty data.table (0 rows) of 2 cols: nb,pval
## [1] NaN
## [1] NA

## Warning: Removed 16 rows containing missing values (geom_point).

## Warning: Removed 16 rows containing missing values (geom_errorbar).

Influence of Playtime on Subjective Difficulty Error

For all groups, motor, sensitive and logical

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTM)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.75587  -0.18208   0.01722   0.17996   0.67980  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.07834    0.02339   3.350  0.00083 ***
## timeNorm     0.01356    0.02393   0.567  0.57104    
## obj.diff    -0.19206    0.03147  -6.103 1.35e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.05882497)
## 
##     Null deviance: 82.357  on 1362  degrees of freedom
## Residual deviance: 80.002  on 1360  degrees of freedom
## AIC: 11.387
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTS)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.81669  -0.17979  -0.04371   0.21432   0.82084  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.04155    0.01869   2.223   0.0264 *  
## timeNorm     0.05613    0.02483   2.261   0.0239 *  
## obj.diff    -0.27648    0.01919 -14.407   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06951042)
## 
##     Null deviance: 115.46  on 1449  degrees of freedom
## Residual deviance: 100.58  on 1447  degrees of freedom
## AIC: 253.81
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTL)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.74305  -0.21400  -0.02148   0.20096   0.71922  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.20615    0.02036  10.127  < 2e-16 ***
## timeNorm     0.06739    0.02531   2.662  0.00785 ** 
## obj.diff    -0.51720    0.02162 -23.927  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.07044787)
## 
##     Null deviance: 151.98  on 1507  degrees of freedom
## Residual deviance: 106.02  on 1505  degrees of freedom
## AIC: 283.97
## 
## Number of Fisher Scoring iterations: 2

##     time.bin subj.diff.mean obj.diff.mean   error.diff   n    pval
##  1:      1.5      0.5414894     0.5916709 -0.041797918  94 0.16 :(
##  2:      4.5      0.5347518     0.5750233 -0.031880263 141 0.16 :(
##  3:      7.5      0.5085106     0.5313589 -0.018533292 141 0.41 :(
##  4:     10.5      0.5404255     0.5341000  0.017669339 141 0.43 :(
##  5:     13.5      0.5085106     0.5167958 -0.006673180 141 0.77 :(
##  6:     16.5      0.5276596     0.5259445  0.002686940 141  0.9 :(
##  7:     19.5      0.4971631     0.5307814 -0.035626571 141 0.081 .
##  8:     22.5      0.4737589     0.4890926 -0.014471502 141  0.5 :(
##  9:     25.5      0.4758865     0.4723221  0.005367814 141 0.81 :(
## 10:     28.5      0.4574468     0.4526413  0.002528689 141 0.88 :(
##     time   error.diff shapes
##  1:  1.5 -0.041797918     16
##  2:  4.5 -0.031880263     16
##  3:  7.5 -0.018533292     16
##  4: 10.5  0.017669339     16
##  5: 13.5 -0.006673180     16
##  6: 16.5  0.002686940     16
##  7: 19.5 -0.035626571     16
##  8: 22.5 -0.014471502     16
##  9: 25.5  0.005367814     16
## 10: 28.5  0.002528689     16

##     time.bin subj.diff.mean obj.diff.mean  error.diff   n        pval
##  1:      1.5      0.4630000     0.5983941 -0.14651688 100 1.5e-05 ***
##  2:      4.5      0.5066667     0.6266344 -0.10334332 150 1.9e-07 ***
##  3:      7.5      0.4593333     0.5432511 -0.08184248 150 0.00012 ***
##  4:     10.5      0.5133333     0.5865266 -0.06779092 150 0.00047 ***
##  5:     13.5      0.4680000     0.5743050 -0.09318691 150 8.6e-07 ***
##  6:     16.5      0.4200000     0.5144528 -0.09805626 150 1.2e-05 ***
##  7:     19.5      0.4826667     0.5502108 -0.05427657 150   0.0014 **
##  8:     22.5      0.4940000     0.5704597 -0.06446875 150   0.0018 **
##  9:     25.5      0.5406667     0.5923116 -0.03496485 150     0.044 *
## 10:     28.5      0.4966667     0.5699890 -0.06718285 150   0.0014 **
##     time  error.diff shapes
##  1:  1.5 -0.14651688     24
##  2:  4.5 -0.10334332     24
##  3:  7.5 -0.08184248     24
##  4: 10.5 -0.06779092     24
##  5: 13.5 -0.09318691     24
##  6: 16.5 -0.09805626     24
##  7: 19.5 -0.05427657     24
##  8: 22.5 -0.06446875     24
##  9: 25.5 -0.03496485     24
## 10: 28.5 -0.06718285     24

##     time.bin subj.diff.mean obj.diff.mean   error.diff   n        pval
##  1:      1.5      0.4355769     0.5969130 -0.167868594 104 3.2e-06 ***
##  2:      4.5      0.5089744     0.6297636 -0.133783305 156 3.6e-06 ***
##  3:      7.5      0.5102564     0.5544687 -0.055654906 156     0.036 *
##  4:     10.5      0.5224359     0.5229882 -0.002890341 156     0.89 :(
##  5:     13.5      0.5173077     0.5312208 -0.020469231 156     0.44 :(
##  6:     16.5      0.5102564     0.5008164  0.003037161 156     0.91 :(
##  7:     19.5      0.4576923     0.4456698  0.001732469 156     0.95 :(
##  8:     22.5      0.4211538     0.4198655 -0.005262489 156     0.84 :(
##  9:     25.5      0.4576923     0.3963862  0.067707055 156     0.015 *
## 10:     28.5      0.4435897     0.3637653  0.061919707 156     0.014 *
##     time   error.diff shapes
##  1:  1.5 -0.167868594     24
##  2:  4.5 -0.133783305     24
##  3:  7.5 -0.055654906     24
##  4: 10.5 -0.002890341     16
##  5: 13.5 -0.020469231     16
##  6: 16.5  0.003037161     16
##  7: 19.5  0.001732469     16
##  8: 22.5 -0.005262489     16
##  9: 25.5  0.067707055     24
## 10: 28.5  0.061919707     24

For all taks, per group

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTAll[niveau.group == "bad"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.80551  -0.22778  -0.01192   0.22190   0.64994  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.13910    0.03545   3.924 9.82e-05 ***
## timeNorm     0.07343    0.03799   1.933   0.0538 .  
## obj.diff    -0.40239    0.03628 -11.091  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06161394)
## 
##     Null deviance: 41.773  on 550  degrees of freedom
## Residual deviance: 33.764  on 548  degrees of freedom
## AIC: 33.098
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTAll[niveau.group == "medium"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.77208  -0.21157  -0.01517   0.21882   0.79312  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.11449    0.02030   5.641 1.97e-08 ***
## timeNorm     0.06792    0.02335   2.909  0.00367 ** 
## obj.diff    -0.36578    0.02215 -16.517  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.07506181)
## 
##     Null deviance: 157.08  on 1797  degrees of freedom
## Residual deviance: 134.74  on 1795  degrees of freedom
## AIC: 451.68
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTAll[niveau.group == "good"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.74123  -0.18968  -0.01653   0.20640   0.79038  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.12866    0.01632   7.883 5.24e-15 ***
## timeNorm     0.03997    0.02096   1.907   0.0567 .  
## obj.diff    -0.34206    0.02051 -16.681  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06485756)
## 
##     Null deviance: 147.53  on 1971  degrees of freedom
## Residual deviance: 127.70  on 1969  degrees of freedom
## AIC: 206.76
## 
## Number of Fisher Scoring iterations: 2

##     time.bin subj.diff.mean obj.diff.mean  error.diff  n        pval
##  1:      1.5      0.5500000     0.7164991 -0.17979307 38 0.00047 ***
##  2:      4.5      0.5894737     0.7384333 -0.15252706 57 0.00076 ***
##  3:      7.5      0.6000000     0.7384966 -0.14438399 57 0.00038 ***
##  4:     10.5      0.6333333     0.7408251 -0.10934836 57   0.0066 **
##  5:     13.5      0.6315789     0.7391129 -0.10056156 57   0.0012 **
##  6:     16.5      0.5508772     0.6713525 -0.13850885 57   0.0012 **
##  7:     19.5      0.6105263     0.6860394 -0.07988988 57     0.036 *
##  8:     22.5      0.6736842     0.7490418 -0.06362996 57     0.16 :(
##  9:     25.5      0.5929825     0.6997122 -0.08557709 57     0.024 *
## 10:     28.5      0.5894737     0.6457104 -0.03589357 57     0.37 :(
##     time  error.diff shapes
##  1:  1.5 -0.17979307     24
##  2:  4.5 -0.15252706     24
##  3:  7.5 -0.14438399     24
##  4: 10.5 -0.10934836     24
##  5: 13.5 -0.10056156     24
##  6: 16.5 -0.13850885     24
##  7: 19.5 -0.07988988     24
##  8: 22.5 -0.06362996     16
##  9: 25.5 -0.08557709     24
## 10: 28.5 -0.03589357     16

##     time.bin subj.diff.mean obj.diff.mean  error.diff   n        pval
##  1:      1.5      0.5072581     0.6713410 -0.17074125 124 1.9e-07 ***
##  2:      4.5      0.5543011     0.6940596 -0.13985266 186 1.2e-09 ***
##  3:      7.5      0.5188172     0.5768179 -0.06264752 186   0.0041 **
##  4:     10.5      0.5586022     0.6108815 -0.04910244 186     0.038 *
##  5:     13.5      0.5381720     0.5989993 -0.06076777 186   0.0047 **
##  6:     16.5      0.5274194     0.5803725 -0.05166961 186     0.015 *
##  7:     19.5      0.5284946     0.5919468 -0.06136287 186   0.0017 **
##  8:     22.5      0.4655914     0.5472381 -0.09086165 186 0.00013 ***
##  9:     25.5      0.5500000     0.5646910 -0.01404629 186      0.5 :(
## 10:     28.5      0.5344086     0.5537926 -0.03027723 186     0.12 :(
##     time  error.diff shapes
##  1:  1.5 -0.17074125     24
##  2:  4.5 -0.13985266     24
##  3:  7.5 -0.06264752     24
##  4: 10.5 -0.04910244     24
##  5: 13.5 -0.06076777     24
##  6: 16.5 -0.05166961     24
##  7: 19.5 -0.06136287     24
##  8: 22.5 -0.09086165     24
##  9: 25.5 -0.01404629     16
## 10: 28.5 -0.03027723     16

##     time.bin subj.diff.mean obj.diff.mean  error.diff   n      pval
##  1:      1.5      0.4316176     0.4931042 -0.05447428 136   0.033 *
##  2:      4.5      0.4612745     0.5006411 -0.03834264 204   0.054 .
##  3:      7.5      0.4387255     0.4584507 -0.02137128 204   0.28 :(
##  4:     10.5      0.4642157     0.4363836  0.02708752 204   0.14 :(
##  5:     13.5      0.4240196     0.4330446 -0.01045530 204   0.62 :(
##  6:     16.5      0.4289216     0.4080249  0.01364465 204   0.49 :(
##  7:     19.5      0.3960784     0.3808330  0.00580766 204   0.78 :(
##  8:     22.5      0.4000000     0.3703351  0.02452680 204   0.19 :(
##  9:     25.5      0.4093137     0.3547269  0.04980681 204 0.0086 **
## 10:     28.5      0.3686275     0.3247904  0.03019069 204   0.13 :(
##     time  error.diff shapes
##  1:  1.5 -0.05447428     24
##  2:  4.5 -0.03834264     16
##  3:  7.5 -0.02137128     16
##  4: 10.5  0.02708752     16
##  5: 13.5 -0.01045530     16
##  6: 16.5  0.01364465     16
##  7: 19.5  0.00580766     16
##  8: 22.5  0.02452680     16
##  9: 25.5  0.04980681     24
## 10: 28.5  0.03019069     16

Per group, motor task

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTM[niveau.group == "bad"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.72641  -0.18104   0.07896   0.17901   0.32506  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  0.17441    0.11392   1.531   0.1280  
## timeNorm     0.05973    0.06311   0.946   0.3455  
## obj.diff    -0.34358    0.13212  -2.600   0.0103 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.04409487)
## 
##     Null deviance: 6.6352  on 144  degrees of freedom
## Residual deviance: 6.2615  on 142  degrees of freedom
## AIC: -36.144
## 
## Number of Fisher Scoring iterations: 2

##     time.bin subj.diff.mean obj.diff.mean    error.diff  n    pval
##  1:      1.5      0.7000000     0.8422160 -0.1313282577 10 0.084 .
##  2:      4.5      0.7200000     0.8045511 -0.0795853843 15 0.49 :(
##  3:      7.5      0.6933333     0.7637930 -0.0692528767 15 0.25 :(
##  4:     10.5      0.7200000     0.7894410 -0.0625540262 15 0.36 :(
##  5:     13.5      0.7000000     0.8006171 -0.1084499111 15 0.055 .
##  6:     16.5      0.7200000     0.7661172 -0.0140493196 15  0.8 :(
##  7:     19.5      0.7466667     0.7396280  0.0120888681 15  0.8 :(
##  8:     22.5      0.7333333     0.7489324 -0.0006995685 15    1 :(
##  9:     25.5      0.7533333     0.8163298 -0.0314486706 15  0.6 :(
## 10:     28.5      0.6866667     0.7440259 -0.0101905199 15 0.85 :(
##     time    error.diff shapes
##  1:  1.5 -0.1313282577     16
##  2:  4.5 -0.0795853843     16
##  3:  7.5 -0.0692528767     16
##  4: 10.5 -0.0625540262     16
##  5: 13.5 -0.1084499111     16
##  6: 16.5 -0.0140493196     16
##  7: 19.5  0.0120888681     16
##  8: 22.5 -0.0006995685     16
##  9: 25.5 -0.0314486706     16
## 10: 28.5 -0.0101905199     16

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTM[niveau.group == "medium"])
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.7008  -0.1756   0.0104   0.2007   0.6764  
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.129171   0.042821   3.017  0.00266 ** 
## timeNorm    -0.001606   0.039220  -0.041  0.96736    
## obj.diff    -0.312573   0.058219  -5.369 1.13e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06994394)
## 
##     Null deviance: 44.480  on 608  degrees of freedom
## Residual deviance: 42.386  on 606  degrees of freedom
## AIC: 113.28
## 
## Number of Fisher Scoring iterations: 2

##     time.bin subj.diff.mean obj.diff.mean   error.diff  n      pval
##  1:      1.5      0.5214286     0.6226413 -0.094272559 42   0.054 .
##  2:      4.5      0.5476190     0.6192837 -0.065077266 63   0.076 .
##  3:      7.5      0.5222222     0.5488374 -0.021946708 63   0.54 :(
##  4:     10.5      0.5269841     0.5633358 -0.017849848 63   0.63 :(
##  5:     13.5      0.5365079     0.5457213 -0.003702683 63   0.93 :(
##  6:     16.5      0.5285714     0.5497442 -0.024443456 63    0.5 :(
##  7:     19.5      0.4698413     0.5571074 -0.092288817 63 0.0066 **
##  8:     22.5      0.4412698     0.5026156 -0.066421840 63   0.067 .
##  9:     25.5      0.4777778     0.4906858 -0.015672991 63   0.67 :(
## 10:     28.5      0.4777778     0.4965908 -0.024207547 63   0.42 :(
##     time   error.diff shapes
##  1:  1.5 -0.094272559     16
##  2:  4.5 -0.065077266     16
##  3:  7.5 -0.021946708     16
##  4: 10.5 -0.017849848     16
##  5: 13.5 -0.003702683     16
##  6: 16.5 -0.024443456     16
##  7: 19.5 -0.092288817     24
##  8: 22.5 -0.066421840     16
##  9: 25.5 -0.015672991     16
## 10: 28.5 -0.024207547     16

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTM[niveau.group == "good"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.66104  -0.16469  -0.00053   0.17110   0.56752  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.003495   0.031165   0.112    0.911
## timeNorm    0.030048   0.032813   0.916    0.360
## obj.diff    0.014011   0.048027   0.292    0.771
## 
## (Dispersion parameter for gaussian family taken to be 0.04854566)
## 
##     Null deviance: 29.460  on 608  degrees of freedom
## Residual deviance: 29.419  on 606  degrees of freedom
## AIC: -109.12
## 
## Number of Fisher Scoring iterations: 2

##     time.bin subj.diff.mean obj.diff.mean   error.diff  n    pval
##  1:      1.5      0.5238095     0.5010468  0.029289623 42 0.44 :(
##  2:      4.5      0.4777778     0.4761135  0.006623743 63 0.82 :(
##  3:      7.5      0.4507937     0.4585390 -0.004803129 63  0.9 :(
##  4:     10.5      0.5111111     0.4440686  0.079755086 63 0.014 *
##  5:     13.5      0.4349206     0.4202938  0.019074305 63 0.57 :(
##  6:     16.5      0.4809524     0.4449609  0.036542116 63 0.21 :(
##  7:     19.5      0.4650794     0.4547299  0.003697701 63 0.89 :(
##  8:     22.5      0.4444444     0.4137030  0.029021771 63 0.27 :(
##  9:     25.5      0.4079365     0.3720518  0.034615081 63 0.19 :(
## 10:     28.5      0.3825397     0.3393145  0.035297116 63 0.18 :(
##     time   error.diff shapes
##  1:  1.5  0.029289623     16
##  2:  4.5  0.006623743     16
##  3:  7.5 -0.004803129     16
##  4: 10.5  0.079755086     24
##  5: 13.5  0.019074305     16
##  6: 16.5  0.036542116     16
##  7: 19.5  0.003697701     16
##  8: 22.5  0.029021771     16
##  9: 25.5  0.034615081     16
## 10: 28.5  0.035297116     16

Per group, sensory task

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTS[niveau.group == "bad"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.79170  -0.22815  -0.02594   0.22945   0.66500  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.12235    0.03937   3.108  0.00203 ** 
## timeNorm     0.07607    0.04697   1.620  0.10616    
## obj.diff    -0.40201    0.03940 -10.203  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06464184)
## 
##     Null deviance: 31.141  on 376  degrees of freedom
## Residual deviance: 24.176  on 374  degrees of freedom
## AIC: 42.305
## 
## Number of Fisher Scoring iterations: 2

##     time.bin subj.diff.mean obj.diff.mean  error.diff  n      pval
##  1:      1.5      0.4961538     0.6466973 -0.16598859 26   0.011 *
##  2:      4.5      0.5461538     0.6931430 -0.15083146 39 0.0035 **
##  3:      7.5      0.5692308     0.7162191 -0.16082632 39  0.002 **
##  4:     10.5      0.6000000     0.7033832 -0.10206205 39   0.036 *
##  5:     13.5      0.6128205     0.7150475 -0.09234508 39   0.026 *
##  6:     16.5      0.4897436     0.6162529 -0.15463914 39 0.0075 **
##  7:     19.5      0.5410256     0.6459039 -0.11889760 39   0.028 *
##  8:     22.5      0.6923077     0.7502411 -0.04716618 39    0.4 :(
##  9:     25.5      0.5512821     0.6511959 -0.08808730 39   0.082 .
## 10:     28.5      0.5589744     0.6283476 -0.05670973 39   0.24 :(
##     time  error.diff shapes
##  1:  1.5 -0.16598859     24
##  2:  4.5 -0.15083146     24
##  3:  7.5 -0.16082632     24
##  4: 10.5 -0.10206205     24
##  5: 13.5 -0.09234508     24
##  6: 16.5 -0.15463914     24
##  7: 19.5 -0.11889760     24
##  8: 22.5 -0.04716618     16
##  9: 25.5 -0.08808730     16
## 10: 28.5 -0.05670973     16

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTS[niveau.group == "medium"])
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.8010  -0.1602  -0.0070   0.1883   0.8299  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.01962    0.02788   0.704   0.4818    
## timeNorm     0.06313    0.03687   1.712   0.0873 .  
## obj.diff    -0.22358    0.02870  -7.790 2.46e-14 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.07348453)
## 
##     Null deviance: 55.675  on 695  degrees of freedom
## Residual deviance: 50.925  on 693  degrees of freedom
## AIC: 163.12
## 
## Number of Fisher Scoring iterations: 2

##     time.bin subj.diff.mean obj.diff.mean   error.diff  n        pval
##  1:      1.5      0.4833333     0.6172699 -0.143174886 48   0.0045 **
##  2:      4.5      0.5569444     0.6703581 -0.082512670 72 0.00014 ***
##  3:      7.5      0.4277778     0.4814532 -0.059902963 72     0.038 *
##  4:     10.5      0.5291667     0.6061622 -0.063855334 72      0.02 *
##  5:     13.5      0.4666667     0.5570867 -0.074746275 72   0.0036 **
##  6:     16.5      0.4152778     0.4912136 -0.068363270 72     0.019 *
##  7:     19.5      0.5222222     0.5512350 -0.009015763 72     0.66 :(
##  8:     22.5      0.4069444     0.5030260 -0.097542924 72   0.0027 **
##  9:     25.5      0.5680556     0.6007228 -0.021474609 72     0.34 :(
## 10:     28.5      0.5361111     0.5833722 -0.048905251 72     0.061 .
##     time   error.diff shapes
##  1:  1.5 -0.143174886     24
##  2:  4.5 -0.082512670     24
##  3:  7.5 -0.059902963     24
##  4: 10.5 -0.063855334     24
##  5: 13.5 -0.074746275     24
##  6: 16.5 -0.068363270     24
##  7: 19.5 -0.009015763     16
##  8: 22.5 -0.097542924     24
##  9: 25.5 -0.021474609     16
## 10: 28.5 -0.048905251     16

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTS[niveau.group == "good"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.67969  -0.14493  -0.06193   0.24483   0.77309  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.03665    0.03281   1.117    0.265    
## timeNorm     0.02377    0.04690   0.507    0.613    
## obj.diff    -0.28843    0.03579  -8.059 1.05e-14 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06447889)
## 
##     Null deviance: 28.311  on 376  degrees of freedom
## Residual deviance: 24.115  on 374  degrees of freedom
## AIC: 41.353
## 
## Number of Fisher Scoring iterations: 2

##     time.bin subj.diff.mean obj.diff.mean  error.diff  n      pval
##  1:      1.5      0.3923077     0.5152434 -0.11294714 26   0.046 *
##  2:      4.5      0.3743590     0.4794053 -0.08723250 39   0.027 *
##  3:      7.5      0.4076923     0.4843714 -0.06343241 39   0.093 .
##  4:     10.5      0.3974359     0.4334195 -0.04546502 39   0.15 :(
##  5:     13.5      0.3256410     0.4653501 -0.14055051 39 0.0019 **
##  6:     16.5      0.3589744     0.4555558 -0.09769237 39 0.0072 **
##  7:     19.5      0.3512821     0.4526269 -0.08381315 39 0.0024 **
##  8:     22.5      0.4564103     0.5151715 -0.02865573 39   0.42 :(
##  9:     25.5      0.4794872     0.5178990 -0.02246320 39   0.48 :(
## 10:     28.5      0.3615385     0.4869228 -0.11144309 39 0.0018 **
##     time  error.diff shapes
##  1:  1.5 -0.11294714     24
##  2:  4.5 -0.08723250     24
##  3:  7.5 -0.06343241     16
##  4: 10.5 -0.04546502     16
##  5: 13.5 -0.14055051     24
##  6: 16.5 -0.09769237     24
##  7: 19.5 -0.08381315     24
##  8: 22.5 -0.02865573     16
##  9: 25.5 -0.02246320     16
## 10: 28.5 -0.11144309     24

Per group, logical task

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTL[niveau.group == "bad"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.42080  -0.06364  -0.00541   0.05325   0.53804  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   0.6438     0.3035   2.122 0.043558 *  
## timeNorm     -0.1953     0.1980  -0.987 0.332949    
## obj.diff     -1.0479     0.2652  -3.951 0.000532 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.04892766)
## 
##     Null deviance: 2.2775  on 28  degrees of freedom
## Residual deviance: 1.2721  on 26  degrees of freedom
## AIC: -0.37331
## 
## Number of Fisher Scoring iterations: 2

## Warning in wilcox.test.default(subj.diff, obj.diff, conf.int = T, paired =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, obj.diff, conf.int = T, paired =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, obj.diff, conf.int = T, paired =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, obj.diff, conf.int = T, paired =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, obj.diff, conf.int = T, paired =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, obj.diff, conf.int = T, paired =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, obj.diff, conf.int = T, paired =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, obj.diff, conf.int = T, paired =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, obj.diff, conf.int = T, paired =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, obj.diff, conf.int = T, paired =
## T): requested conf.level not achievable

##     time.bin subj.diff.mean obj.diff.mean  error.diff n    pval
##  1:      1.5      0.5000000     0.9953377 -0.49533771 2  0.5 :(
##  2:      4.5      0.5000000     0.9966183 -0.49676704 3 0.25 :(
##  3:      7.5      0.5333333     0.9016221 -0.39520236 3 0.25 :(
##  4:     10.5      0.6333333     0.9844904 -0.37981003 3 0.25 :(
##  5:     13.5      0.5333333     0.7444412 -0.19033428 3 0.25 :(
##  6:     16.5      0.5000000     0.9138248 -0.41045855 3 0.25 :(
##  7:     19.5      0.8333333     0.9398589 -0.06558618 3    1 :(
##  8:     22.5      0.1333333     0.7339989 -0.58430847 3 0.25 :(
##  9:     25.5      0.3333333     0.7473360 -0.35421622 3 0.25 :(
## 10:     28.5      0.5000000     0.3798491  0.12306266 3 0.25 :(
##     time  error.diff shapes
##  1:  1.5 -0.49533771     16
##  2:  4.5 -0.49676704     16
##  3:  7.5 -0.39520236     16
##  4: 10.5 -0.37981003     16
##  5: 13.5 -0.19033428     16
##  6: 16.5 -0.41045855     16
##  7: 19.5 -0.06558618     16
##  8: 22.5 -0.58430847     16
##  9: 25.5 -0.35421622     16
## 10: 28.5  0.12306266     16

## Warning: Removed 4 rows containing missing values (geom_point).

## Warning: Removed 9 rows containing missing values (geom_errorbar).

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTL[niveau.group == "medium"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.67353  -0.15262  -0.07006   0.25156   0.53407  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.41580    0.04430   9.386   <2e-16 ***
## timeNorm     0.07754    0.04298   1.804   0.0718 .  
## obj.diff    -0.77914    0.04385 -17.768   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06642515)
## 
##     Null deviance: 56.272  on 492  degrees of freedom
## Residual deviance: 32.548  on 490  degrees of freedom
## AIC: 67.206
## 
## Number of Fisher Scoring iterations: 2

##     time.bin subj.diff.mean obj.diff.mean   error.diff  n        pval
##  1:      1.5      0.5235294     0.8078351 -0.294649608 34 2.5e-06 ***
##  2:      4.5      0.5588235     0.8198910 -0.279089899 51 6.9e-07 ***
##  3:      7.5      0.6431373     0.7460147 -0.129751064 51      0.02 *
##  4:     10.5      0.6392157     0.6762768 -0.053077182 51     0.32 :(
##  5:     13.5      0.6411765     0.7239841 -0.115051222 51     0.079 .
##  6:     16.5      0.6843137     0.7440789 -0.065019768 51     0.18 :(
##  7:     19.5      0.6098039     0.6924592 -0.090749554 51     0.038 *
##  8:     22.5      0.5784314     0.6647773 -0.093252093 51     0.067 .
##  9:     25.5      0.6137255     0.6052408  0.015437858 51     0.79 :(
## 10:     28.5      0.6019608     0.5826942 -0.002540562 51     0.94 :(
##     time   error.diff shapes
##  1:  1.5 -0.294649608     24
##  2:  4.5 -0.279089899     24
##  3:  7.5 -0.129751064     24
##  4: 10.5 -0.053077182     16
##  5: 13.5 -0.115051222     16
##  6: 16.5 -0.065019768     16
##  7: 19.5 -0.090749554     24
##  8: 22.5 -0.093252093     16
##  9: 25.5  0.015437858     16
## 10: 28.5 -0.002540562     16

## 
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff, 
##     data = DTL[niveau.group == "good"])
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.6558  -0.2242  -0.0137   0.2082   0.7510  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.17158    0.02426   7.072 2.89e-12 ***
## timeNorm     0.05491    0.03137   1.751   0.0803 .  
## obj.diff    -0.43800    0.02984 -14.677  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.06882334)
## 
##     Null deviance: 85.286  on 985  degrees of freedom
## Residual deviance: 67.653  on 983  degrees of freedom
## AIC: 164.4
## 
## Number of Fisher Scoring iterations: 2

##     time.bin subj.diff.mean obj.diff.mean  error.diff   n      pval
##  1:      1.5      0.3897059     0.4797335 -0.09419071  68   0.035 *
##  2:      4.5      0.4843137     0.5239101 -0.04626564 102   0.15 :(
##  3:      7.5      0.4431373     0.4484853 -0.01805861 102   0.59 :(
##  4:     10.5      0.4607843     0.4327702  0.02458129 102   0.36 :(
##  5:     13.5      0.4549020     0.4285680  0.02633160 102   0.47 :(
##  6:     16.5      0.4235294     0.3670379  0.04549823 102   0.11 :(
##  7:     19.5      0.3705882     0.3077402  0.05923864 102   0.082 .
##  8:     22.5      0.3509804     0.2881703  0.05278595 102   0.084 .
##  9:     25.5      0.3833333     0.2816368  0.10186497 102 0.0018 **
## 10:     28.5      0.3627451     0.2538279  0.10288965 102 0.0048 **
##     time  error.diff shapes
##  1:  1.5 -0.09419071     24
##  2:  4.5 -0.04626564     16
##  3:  7.5 -0.01805861     16
##  4: 10.5  0.02458129     16
##  5: 13.5  0.02633160     16
##  6: 16.5  0.04549823     16
##  7: 19.5  0.05923864     16
##  8: 22.5  0.05278595     16
##  9: 25.5  0.10186497     24
## 10: 28.5  0.10288965     24

Link between d(t-1) and error

## Warning: Removed 28 rows containing missing values (geom_path).

## geom_path: Each group consists of only one observation. Do you need to
## adjust the group aesthetic?

## Warning: Removed 28 rows containing missing values (geom_path).

## geom_path: Each group consists of only one observation. Do you need to
## adjust the group aesthetic?

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.2613, p-value = 0.2072
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.02303803

## [1] "0.023 0.21 :("

## Warning: Removed 28 rows containing missing values (geom_path).

## geom_path: Each group consists of only one observation. Do you need to
## adjust the group aesthetic?

## Warning: Removed 28 rows containing missing values (geom_path).

## geom_path: Each group consists of only one observation. Do you need to
## adjust the group aesthetic?

## Warning: Removed 1 rows containing missing values (geom_path).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.92042, p-value = 0.3574
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.01642902

## [1] "0.016 0.36 :("

## Warning: Removed 28 rows containing missing values (geom_path).

## geom_path: Each group consists of only one observation. Do you need to
## adjust the group aesthetic?

## Warning: Removed 28 rows containing missing values (geom_path).

## geom_path: Each group consists of only one observation. Do you need to
## adjust the group aesthetic?

## Warning: Removed 26 rows containing missing values (geom_path).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 3.2064, p-value = 0.001344
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.05646457 
## 
## [1] "DTL 0.056 0.0013 **"

## [1] "0.056 0.0013 **"

Subjective difficulty and play habits

Playing video game in general and subjective difficulty error

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -1.9512, p-value = 0.05103
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.1244062 
## 
## [1] "pvg.on.error -0.12 0.051 ."

Playing board game in general and subjective difficulty error

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -2.611, p-value = 0.009028
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.1640952 
## 
## [1] "pbg.on.error -0.16 0.009 **"

In game level and subjective difficulty error

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.4956, p-value = 0.1348
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.08314103

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## T = 516, p-value = 0.6615
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.0453284

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.1962, p-value = 0.2316
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1167347

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.7676, p-value = 0.07713
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1689291 
## 
## [1] "niveau.group.on.error.l 0.17 0.077 ."

Sex and subjective difficulty error

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 3.4092, p-value = 0.0006514
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.2309816 
## 
## [1] "sexe.on.error 0.23 0.00065 ***"

## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.8377, p-value = 0.06611
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.2235683 
## 
## [1] "sexe.on.error.m 0.22 0.066 ."

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 2.1421, p-value = 0.03218
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.2523479 
## 
## [1] "sexe.on.error.s 0.25 0.032 *"

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.9042, p-value = 0.05688
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##      tau 
## 0.219795 
## 
## [1] "sexe.on.error.l 0.22 0.057 ."

## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  B and A
## W = 3351, p-value = 0.0006562
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  0.01887946 0.09421108
## sample estimates:
## difference in location 
##             0.05808392 
## 
## [1] "sexe.on.error.2 0.058 0.00066 *** mean(A): -0.057 mean(B): -0.00063"

## 
##  Wilcoxon rank sum test
## 
## data:  B and A
## W = 338, p-value = 0.06742
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.002139508  0.112594322
## sample estimates:
## difference in location 
##             0.05082363 
## 
## [1] "sexe.on.error.m.2 0.051 0.067 . mean(A): -0.052 mean(B): 0.0015"

## 
##  Wilcoxon rank sum test
## 
## data:  B and A
## W = 375, p-value = 0.032
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  0.004691112 0.132198685
## sample estimates:
## difference in location 
##             0.06809898 
## 
## [1] "sexe.on.error.s.2 0.068 0.032 * mean(A): -0.058 mean(B): 0.008"

## 
##  Wilcoxon rank sum test
## 
## data:  B and A
## W = 405, p-value = 0.05764
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.001446701  0.111180500
## sample estimates:
## difference in location 
##             0.05042229 
## 
## [1] "sexe.on.error.l.2 0.05 0.058 . mean(A): -0.061 mean(B): -0.01"

Risk aversion and subjective difficulty error

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.69771, p-value = 0.4854
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.04215397

## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.43467, p-value = 0.6638
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.04748814

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.73768, p-value = 0.4607
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.07731606

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.85186, p-value = 0.3943
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.08786374

Self efficacy and subjective difficulty error

## Warning: Removed 72 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -3.3268, p-value = 0.0008786
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.2704897 
## 
## [1] "self.eff.on.error -0.27 0.00088 ***"

## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties

## Warning: Removed 23 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -1.8964, p-value = 0.0579
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.2826236 
## 
## [1] "self.eff.on.error -0.28 0.058 ."

## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties

## Warning in cor.test.default(Y, X, method = "kendall"): Removed 23 rows
## containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -1.7201, p-value = 0.08542
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.2402101 
## 
## [1] "self.eff.on.error -0.24 0.085 ."

## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties

## Warning: Removed 26 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -2.0192, p-value = 0.04346
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.2880916 
## 
## [1] "self.eff.on.error -0.29 0.043 *"